Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations336776
Missing cells46595
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.8 MiB
Average record size in memory426.1 B

Variable types

Categorical3
Numeric13
Text2
DateTime1

Alerts

year has constant value "2013" Constant
air_time is highly overall correlated with distanceHigh correlation
arr_delay is highly overall correlated with dep_delayHigh correlation
arr_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
carrier is highly overall correlated with originHigh correlation
dep_delay is highly overall correlated with arr_delayHigh correlation
dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
distance is highly overall correlated with air_timeHigh correlation
hour is highly overall correlated with arr_time and 3 other fieldsHigh correlation
origin is highly overall correlated with carrierHigh correlation
sched_arr_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
sched_dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
dep_time has 8255 (2.5%) missing values Missing
dep_delay has 8255 (2.5%) missing values Missing
arr_time has 8713 (2.6%) missing values Missing
arr_delay has 9430 (2.8%) missing values Missing
air_time has 9430 (2.8%) missing values Missing
dep_delay has 16514 (4.9%) zeros Zeros
arr_delay has 5409 (1.6%) zeros Zeros
minute has 60696 (18.0%) zeros Zeros

Reproduction

Analysis started2025-05-24 03:42:28.978091
Analysis finished2025-05-24 03:43:32.918363
Duration1 minute and 3.94 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 MiB
2013
336776 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1347104
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 336776
100.0%

Length

2025-05-24T09:13:33.026771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T09:13:33.227471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2013 336776
100.0%

Most occurring characters

ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1347104
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1347104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.54851
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:33.359569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4144572
Coefficient of variation (CV)0.52140979
Kurtosis-1.1869501
Mean6.54851
Median Absolute Deviation (MAD)3
Skewness-0.013399885
Sum2205381
Variance11.658518
MonotonicityNot monotonic
2025-05-24T09:13:33.496130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 29425
8.7%
8 29327
8.7%
10 28889
8.6%
3 28834
8.6%
5 28796
8.6%
4 28330
8.4%
6 28243
8.4%
12 28135
8.4%
9 27574
8.2%
11 27268
8.1%
Other values (2) 51955
15.4%
ValueCountFrequency (%)
1 27004
8.0%
2 24951
7.4%
3 28834
8.6%
4 28330
8.4%
5 28796
8.6%
6 28243
8.4%
7 29425
8.7%
8 29327
8.7%
9 27574
8.2%
10 28889
8.6%
ValueCountFrequency (%)
12 28135
8.4%
11 27268
8.1%
10 28889
8.6%
9 27574
8.2%
8 29327
8.7%
7 29425
8.7%
6 28243
8.4%
5 28796
8.6%
4 28330
8.4%
3 28834
8.6%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.710787
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:33.679281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7686071
Coefficient of variation (CV)0.55812653
Kurtosis-1.1859454
Mean15.710787
Median Absolute Deviation (MAD)8
Skewness0.0077444993
Sum5291016
Variance76.888471
MonotonicityNot monotonic
2025-05-24T09:13:33.833748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 11399
 
3.4%
11 11359
 
3.4%
22 11345
 
3.4%
15 11317
 
3.4%
8 11271
 
3.3%
10 11227
 
3.3%
17 11222
 
3.3%
3 11211
 
3.3%
21 11141
 
3.3%
20 11111
 
3.3%
Other values (21) 224173
66.6%
ValueCountFrequency (%)
1 11036
3.3%
2 10808
3.2%
3 11211
3.3%
4 11059
3.3%
5 10858
3.2%
6 11059
3.3%
7 10985
3.3%
8 11271
3.3%
9 10857
3.2%
10 11227
3.3%
ValueCountFrequency (%)
31 6190
1.8%
30 10289
3.1%
29 10039
3.0%
28 10773
3.2%
27 11084
3.3%
26 10883
3.2%
25 11097
3.3%
24 11041
3.3%
23 10966
3.3%
22 11345
3.4%

dep_time
Real number (ℝ)

High correlation  Missing 

Distinct1318
Distinct (%)0.4%
Missing8255
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1349.1099
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:34.038627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile624
Q1907
median1401
Q31744
95-th percentile2112
Maximum2400
Range2399
Interquartile range (IQR)837

Descriptive statistics

Standard deviation488.28179
Coefficient of variation (CV)0.36192883
Kurtosis-1.08832
Mean1349.1099
Median Absolute Deviation (MAD)428
Skewness-0.024743453
Sum4.4321095 × 108
Variance238419.11
MonotonicityNot monotonic
2025-05-24T09:13:34.204335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 834
 
0.2%
755 820
 
0.2%
556 818
 
0.2%
557 799
 
0.2%
655 798
 
0.2%
1455 774
 
0.2%
1454 769
 
0.2%
654 751
 
0.2%
855 743
 
0.2%
754 742
 
0.2%
Other values (1308) 320673
95.2%
(Missing) 8255
 
2.5%
ValueCountFrequency (%)
1 25
< 0.1%
2 35
< 0.1%
3 26
< 0.1%
4 26
< 0.1%
5 21
< 0.1%
6 22
< 0.1%
7 22
< 0.1%
8 23
< 0.1%
9 28
< 0.1%
10 22
< 0.1%
ValueCountFrequency (%)
2400 29
 
< 0.1%
2359 55
< 0.1%
2358 76
< 0.1%
2357 74
< 0.1%
2356 74
< 0.1%
2355 82
< 0.1%
2354 69
< 0.1%
2353 68
< 0.1%
2352 68
< 0.1%
2351 57
< 0.1%

sched_dep_time
Real number (ℝ)

High correlation 

Distinct1021
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344.2548
Minimum106
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:34.401849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile630
Q1906
median1359
Q31729
95-th percentile2050
Maximum2359
Range2253
Interquartile range (IQR)823

Descriptive statistics

Standard deviation467.33576
Coefficient of variation (CV)0.34765414
Kurtosis-1.1979031
Mean1344.2548
Median Absolute Deviation (MAD)414
Skewness-0.0058580829
Sum4.5271277 × 108
Variance218402.71
MonotonicityNot monotonic
2025-05-24T09:13:34.625112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 7016
 
2.1%
700 4900
 
1.5%
630 4770
 
1.4%
900 4766
 
1.4%
1200 4624
 
1.4%
1700 4526
 
1.3%
1600 4098
 
1.2%
800 3926
 
1.2%
1300 3689
 
1.1%
1900 3653
 
1.1%
Other values (1011) 290808
86.4%
ValueCountFrequency (%)
106 1
 
< 0.1%
500 341
0.1%
501 1
 
< 0.1%
505 2
 
< 0.1%
510 5
 
< 0.1%
515 208
0.1%
516 4
 
< 0.1%
517 28
 
< 0.1%
520 7
 
< 0.1%
525 37
 
< 0.1%
ValueCountFrequency (%)
2359 828
0.2%
2358 44
 
< 0.1%
2355 73
 
< 0.1%
2352 16
 
< 0.1%
2345 1
 
< 0.1%
2339 1
 
< 0.1%
2330 14
 
< 0.1%
2315 1
 
< 0.1%
2305 61
 
< 0.1%
2300 22
 
< 0.1%

dep_delay
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct527
Distinct (%)0.2%
Missing8255
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean12.63907
Minimum-43
Maximum1301
Zeros16514
Zeros (%)4.9%
Negative183575
Negative (%)54.5%
Memory size2.6 MiB
2025-05-24T09:13:35.288436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-43
5-th percentile-9
Q1-5
median-2
Q311
95-th percentile88
Maximum1301
Range1344
Interquartile range (IQR)16

Descriptive statistics

Standard deviation40.210061
Coefficient of variation (CV)3.1814097
Kurtosis43.950116
Mean12.63907
Median Absolute Deviation (MAD)4
Skewness4.8025405
Sum4152200
Variance1616.849
MonotonicityNot monotonic
2025-05-24T09:13:35.574688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 24821
 
7.4%
-4 24619
 
7.3%
-3 24218
 
7.2%
-2 21516
 
6.4%
-6 20701
 
6.1%
-1 18813
 
5.6%
-7 16752
 
5.0%
0 16514
 
4.9%
-8 11791
 
3.5%
1 8050
 
2.4%
Other values (517) 140726
41.8%
(Missing) 8255
 
2.5%
ValueCountFrequency (%)
-43 1
 
< 0.1%
-33 1
 
< 0.1%
-32 1
 
< 0.1%
-30 1
 
< 0.1%
-27 1
 
< 0.1%
-26 1
 
< 0.1%
-25 2
 
< 0.1%
-24 4
 
< 0.1%
-23 6
< 0.1%
-22 11
< 0.1%
ValueCountFrequency (%)
1301 1
< 0.1%
1137 1
< 0.1%
1126 1
< 0.1%
1014 1
< 0.1%
1005 1
< 0.1%
960 1
< 0.1%
911 1
< 0.1%
899 1
< 0.1%
898 1
< 0.1%
896 1
< 0.1%

arr_time
Real number (ℝ)

High correlation  Missing 

Distinct1411
Distinct (%)0.4%
Missing8713
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1502.055
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:35.846864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile736
Q11104
median1535
Q31940
95-th percentile2248
Maximum2400
Range2399
Interquartile range (IQR)836

Descriptive statistics

Standard deviation533.26413
Coefficient of variation (CV)0.35502304
Kurtosis-0.19263438
Mean1502.055
Median Absolute Deviation (MAD)418
Skewness-0.46781906
Sum4.9276867 × 108
Variance284370.63
MonotonicityNot monotonic
2025-05-24T09:13:36.062500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008 485
 
0.1%
1013 484
 
0.1%
1015 479
 
0.1%
1012 464
 
0.1%
1005 460
 
0.1%
1016 459
 
0.1%
1006 459
 
0.1%
1011 457
 
0.1%
1007 456
 
0.1%
1040 455
 
0.1%
Other values (1401) 323405
96.0%
(Missing) 8713
 
2.6%
ValueCountFrequency (%)
1 201
0.1%
2 164
< 0.1%
3 174
0.1%
4 173
0.1%
5 206
0.1%
6 148
< 0.1%
7 170
0.1%
8 147
< 0.1%
9 140
< 0.1%
10 178
0.1%
ValueCountFrequency (%)
2400 150
< 0.1%
2359 222
0.1%
2358 189
0.1%
2357 207
0.1%
2356 202
0.1%
2355 206
0.1%
2354 195
0.1%
2353 182
0.1%
2352 193
0.1%
2351 216
0.1%

sched_arr_time
Real number (ℝ)

High correlation 

Distinct1163
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1536.3802
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:36.248850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile815
Q11124
median1556
Q31945
95-th percentile2246
Maximum2359
Range2358
Interquartile range (IQR)821

Descriptive statistics

Standard deviation497.45714
Coefficient of variation (CV)0.32378518
Kurtosis-0.38224779
Mean1536.3802
Median Absolute Deviation (MAD)417
Skewness-0.35313807
Sum5.1741598 × 108
Variance247463.61
MonotonicityNot monotonic
2025-05-24T09:13:36.453057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025 1324
 
0.4%
2015 1234
 
0.4%
1110 1198
 
0.4%
1115 1193
 
0.4%
1235 1133
 
0.3%
2359 1121
 
0.3%
1815 1111
 
0.3%
1015 1080
 
0.3%
1645 1079
 
0.3%
1220 1073
 
0.3%
Other values (1153) 325230
96.6%
ValueCountFrequency (%)
1 243
0.1%
2 95
 
< 0.1%
3 159
< 0.1%
4 107
< 0.1%
5 82
 
< 0.1%
6 19
 
< 0.1%
7 85
 
< 0.1%
8 154
< 0.1%
9 55
 
< 0.1%
10 72
 
< 0.1%
ValueCountFrequency (%)
2359 1121
0.3%
2358 483
0.1%
2357 349
 
0.1%
2356 468
0.1%
2355 335
 
0.1%
2354 384
 
0.1%
2353 263
 
0.1%
2352 47
 
< 0.1%
2351 140
 
< 0.1%
2350 105
 
< 0.1%

arr_delay
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct577
Distinct (%)0.2%
Missing9430
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean6.8953768
Minimum-86
Maximum1272
Zeros5409
Zeros (%)1.6%
Negative188933
Negative (%)56.1%
Memory size2.6 MiB
2025-05-24T09:13:36.672412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-32
Q1-17
median-5
Q314
95-th percentile91
Maximum1272
Range1358
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.633292
Coefficient of variation (CV)6.4729301
Kurtosis29.233044
Mean6.8953768
Median Absolute Deviation (MAD)14
Skewness3.7168175
Sum2257174
Variance1992.1307
MonotonicityNot monotonic
2025-05-24T09:13:36.888605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13 7177
 
2.1%
-10 7088
 
2.1%
-12 7046
 
2.1%
-14 6975
 
2.1%
-11 6863
 
2.0%
-9 6815
 
2.0%
-15 6796
 
2.0%
-7 6677
 
2.0%
-17 6668
 
2.0%
-8 6663
 
2.0%
Other values (567) 258578
76.8%
(Missing) 9430
 
2.8%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-79 1
 
< 0.1%
-75 2
 
< 0.1%
-74 1
 
< 0.1%
-73 1
 
< 0.1%
-71 3
 
< 0.1%
-70 8
< 0.1%
-69 7
< 0.1%
-68 12
< 0.1%
-67 7
< 0.1%
ValueCountFrequency (%)
1272 1
< 0.1%
1127 1
< 0.1%
1109 1
< 0.1%
1007 1
< 0.1%
989 1
< 0.1%
931 1
< 0.1%
915 1
< 0.1%
895 1
< 0.1%
878 1
< 0.1%
875 1
< 0.1%

carrier
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.9 MiB
UA
58665 
B6
54635 
EV
54173 
DL
48110 
AA
32729 
Other values (11)
88464 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters673552
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowUA
3rd rowAA
4th rowB6
5th rowDL

Common Values

ValueCountFrequency (%)
UA 58665
17.4%
B6 54635
16.2%
EV 54173
16.1%
DL 48110
14.3%
AA 32729
9.7%
MQ 26397
7.8%
US 20536
 
6.1%
9E 18460
 
5.5%
WN 12275
 
3.6%
VX 5162
 
1.5%
Other values (6) 5634
 
1.7%

Length

2025-05-24T09:13:37.048876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua 58665
17.4%
b6 54635
16.2%
ev 54173
16.1%
dl 48110
14.3%
aa 32729
9.7%
mq 26397
7.8%
us 20536
 
6.1%
9e 18460
 
5.5%
wn 12275
 
3.6%
vx 5162
 
1.5%
Other values (6) 5634
 
1.7%

Most occurring characters

ValueCountFrequency (%)
A 125179
18.6%
U 79201
11.8%
E 72633
10.8%
V 59936
8.9%
B 54635
8.1%
6 54635
8.1%
L 51370
7.6%
D 48110
 
7.1%
Q 26397
 
3.9%
M 26397
 
3.9%
Other values (9) 75059
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 599772
89.0%
Decimal Number 73780
 
11.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125179
20.9%
U 79201
13.2%
E 72633
12.1%
V 59936
10.0%
B 54635
9.1%
L 51370
8.6%
D 48110
 
8.0%
Q 26397
 
4.4%
M 26397
 
4.4%
S 21250
 
3.5%
Other values (7) 34664
 
5.8%
Decimal Number
ValueCountFrequency (%)
6 54635
74.1%
9 19145
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 599772
89.0%
Common 73780
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125179
20.9%
U 79201
13.2%
E 72633
12.1%
V 59936
10.0%
B 54635
9.1%
L 51370
8.6%
D 48110
 
8.0%
Q 26397
 
4.4%
M 26397
 
4.4%
S 21250
 
3.5%
Other values (7) 34664
 
5.8%
Common
ValueCountFrequency (%)
6 54635
74.1%
9 19145
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125179
18.6%
U 79201
11.8%
E 72633
10.8%
V 59936
8.9%
B 54635
8.1%
6 54635
8.1%
L 51370
7.6%
D 48110
 
7.1%
Q 26397
 
3.9%
M 26397
 
3.9%
Other values (9) 75059
11.1%

flight
Real number (ℝ)

Distinct3844
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.9236
Minimum1
Maximum8500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:37.251523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile91
Q1553
median1496
Q33465
95-th percentile4695
Maximum8500
Range8499
Interquartile range (IQR)2912

Descriptive statistics

Standard deviation1632.4719
Coefficient of variation (CV)0.82785759
Kurtosis-0.84856068
Mean1971.9236
Median Absolute Deviation (MAD)1085
Skewness0.66160363
Sum6.6409655 × 108
Variance2664964.6
MonotonicityNot monotonic
2025-05-24T09:13:37.528835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 968
 
0.3%
27 898
 
0.3%
181 882
 
0.3%
301 871
 
0.3%
161 786
 
0.2%
695 782
 
0.2%
1109 716
 
0.2%
745 711
 
0.2%
359 709
 
0.2%
1 701
 
0.2%
Other values (3834) 328752
97.6%
ValueCountFrequency (%)
1 701
0.2%
2 51
 
< 0.1%
3 631
0.2%
4 393
0.1%
5 324
0.1%
6 210
 
0.1%
7 237
 
0.1%
8 236
 
0.1%
9 153
 
< 0.1%
10 61
 
< 0.1%
ValueCountFrequency (%)
8500 1
 
< 0.1%
6181 80
< 0.1%
6180 6
 
< 0.1%
6177 164
< 0.1%
6171 1
 
< 0.1%
6168 2
 
< 0.1%
6167 3
 
< 0.1%
6165 1
 
< 0.1%
6140 1
 
< 0.1%
6138 2
 
< 0.1%
Distinct4043
Distinct (%)1.2%
Missing2512
Missing (%)0.7%
Memory size20.2 MiB
2025-05-24T09:13:38.248314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9952223
Min length5

Characters and Unicode

Total characters2003987
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)0.1%

Sample

1st rowN14228
2nd rowN24211
3rd rowN619AA
4th rowN804JB
5th rowN668DN
ValueCountFrequency (%)
n725mq 575
 
0.2%
n722mq 513
 
0.2%
n723mq 507
 
0.2%
n711mq 486
 
0.1%
n713mq 483
 
0.1%
n258jb 427
 
0.1%
n298jb 407
 
0.1%
n353jb 404
 
0.1%
n351jb 402
 
0.1%
n735mq 396
 
0.1%
Other values (4033) 329664
98.6%
2025-05-24T09:13:39.222723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 360812
18.0%
3 149395
 
7.5%
1 145378
 
7.3%
5 135552
 
6.8%
A 118723
 
5.9%
7 114699
 
5.7%
2 110052
 
5.5%
9 106405
 
5.3%
4 102253
 
5.1%
6 101332
 
5.1%
Other values (24) 559386
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1118051
55.8%
Uppercase Letter 885936
44.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 360812
40.7%
A 118723
 
13.4%
B 69014
 
7.8%
J 66843
 
7.5%
U 46341
 
5.2%
W 35652
 
4.0%
Q 29737
 
3.4%
M 27749
 
3.1%
D 24010
 
2.7%
E 14851
 
1.7%
Other values (14) 92204
 
10.4%
Decimal Number
ValueCountFrequency (%)
3 149395
13.4%
1 145378
13.0%
5 135552
12.1%
7 114699
10.3%
2 110052
9.8%
9 106405
9.5%
4 102253
9.1%
6 101332
9.1%
8 84315
7.5%
0 68670
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1118051
55.8%
Latin 885936
44.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 360812
40.7%
A 118723
 
13.4%
B 69014
 
7.8%
J 66843
 
7.5%
U 46341
 
5.2%
W 35652
 
4.0%
Q 29737
 
3.4%
M 27749
 
3.1%
D 24010
 
2.7%
E 14851
 
1.7%
Other values (14) 92204
 
10.4%
Common
ValueCountFrequency (%)
3 149395
13.4%
1 145378
13.0%
5 135552
12.1%
7 114699
10.3%
2 110052
9.8%
9 106405
9.5%
4 102253
9.1%
6 101332
9.1%
8 84315
7.5%
0 68670
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2003987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 360812
18.0%
3 149395
 
7.5%
1 145378
 
7.3%
5 135552
 
6.8%
A 118723
 
5.9%
7 114699
 
5.7%
2 110052
 
5.5%
9 106405
 
5.3%
4 102253
 
5.1%
6 101332
 
5.1%
Other values (24) 559386
27.9%

origin
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
EWR
120835 
JFK
111279 
LGA
104662 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1010328
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowLGA
3rd rowJFK
4th rowJFK
5th rowLGA

Common Values

ValueCountFrequency (%)
EWR 120835
35.9%
JFK 111279
33.0%
LGA 104662
31.1%

Length

2025-05-24T09:13:39.448497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T09:13:39.664816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ewr 120835
35.9%
jfk 111279
33.0%
lga 104662
31.1%

Most occurring characters

ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1010328
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

dest
Text

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
2025-05-24T09:13:40.119497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1010328
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIAH
2nd rowIAH
3rd rowMIA
4th rowBQN
5th rowATL
ValueCountFrequency (%)
ord 17283
 
5.1%
atl 17215
 
5.1%
lax 16174
 
4.8%
bos 15508
 
4.6%
mco 14082
 
4.2%
clt 14064
 
4.2%
sfo 13331
 
4.0%
fll 12055
 
3.6%
mia 11728
 
3.5%
dca 9705
 
2.9%
Other values (95) 195631
58.1%
2025-05-24T09:13:40.838575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1010328
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

air_time
Real number (ℝ)

High correlation  Missing 

Distinct509
Distinct (%)0.2%
Missing9430
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean150.68646
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:41.162795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q182
median129
Q3192
95-th percentile339
Maximum695
Range675
Interquartile range (IQR)110

Descriptive statistics

Standard deviation93.688305
Coefficient of variation (CV)0.62174335
Kurtosis0.86307699
Mean150.68646
Median Absolute Deviation (MAD)51
Skewness1.0707052
Sum49326610
Variance8777.4984
MonotonicityNot monotonic
2025-05-24T09:13:41.439966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 2552
 
0.8%
43 2543
 
0.8%
41 2513
 
0.7%
45 2495
 
0.7%
40 2466
 
0.7%
44 2444
 
0.7%
39 2411
 
0.7%
47 2409
 
0.7%
46 2406
 
0.7%
109 2377
 
0.7%
Other values (499) 302730
89.9%
(Missing) 9430
 
2.8%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 14
 
< 0.1%
22 34
 
< 0.1%
23 82
 
< 0.1%
24 103
< 0.1%
25 124
< 0.1%
26 169
0.1%
27 147
< 0.1%
28 180
0.1%
29 209
0.1%
ValueCountFrequency (%)
695 1
< 0.1%
691 1
< 0.1%
686 2
< 0.1%
683 1
< 0.1%
679 1
< 0.1%
676 2
< 0.1%
675 1
< 0.1%
671 2
< 0.1%
669 1
< 0.1%
667 2
< 0.1%

distance
Real number (ℝ)

High correlation 

Distinct214
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1039.9126
Minimum17
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:41.758450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile199
Q1502
median872
Q31389
95-th percentile2475
Maximum4983
Range4966
Interquartile range (IQR)887

Descriptive statistics

Standard deviation733.23303
Coefficient of variation (CV)0.70509102
Kurtosis1.1936399
Mean1039.9126
Median Absolute Deviation (MAD)384
Skewness1.1286902
Sum3.5021761 × 108
Variance537630.68
MonotonicityNot monotonic
2025-05-24T09:13:42.067796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 11262
 
3.3%
762 10263
 
3.0%
733 8857
 
2.6%
2586 8204
 
2.4%
544 6168
 
1.8%
719 6100
 
1.8%
187 5898
 
1.8%
1096 5781
 
1.7%
2454 5695
 
1.7%
184 5504
 
1.6%
Other values (204) 263044
78.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
80 49
 
< 0.1%
94 976
 
0.3%
96 607
 
0.2%
116 443
 
0.1%
143 439
 
0.1%
160 376
 
0.1%
169 545
 
0.2%
173 221
 
0.1%
184 5504
1.6%
ValueCountFrequency (%)
4983 342
 
0.1%
4963 365
 
0.1%
3370 8
 
< 0.1%
2586 8204
2.4%
2576 312
 
0.1%
2569 329
 
0.1%
2565 5127
1.5%
2521 284
 
0.1%
2475 11262
3.3%
2465 1039
 
0.3%

hour
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.180247
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:42.370905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median13
Q317
95-th percentile20
Maximum23
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6613157
Coefficient of variation (CV)0.3536592
Kurtosis-1.2064161
Mean13.180247
Median Absolute Deviation (MAD)4
Skewness-0.00054265178
Sum4438791
Variance21.727864
MonotonicityNot monotonic
2025-05-24T09:13:42.618821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 27242
 
8.1%
6 25951
 
7.7%
17 24426
 
7.3%
15 23888
 
7.1%
16 23002
 
6.8%
7 22821
 
6.8%
18 21783
 
6.5%
14 21706
 
6.4%
19 21441
 
6.4%
9 20312
 
6.0%
Other values (10) 104204
30.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
5 1953
 
0.6%
6 25951
7.7%
7 22821
6.8%
8 27242
8.1%
9 20312
6.0%
10 16708
5.0%
11 16033
4.8%
12 18181
5.4%
13 19956
5.9%
ValueCountFrequency (%)
23 1061
 
0.3%
22 2639
 
0.8%
21 10933
3.2%
20 16739
5.0%
19 21441
6.4%
18 21783
6.5%
17 24426
7.3%
16 23002
6.8%
15 23888
7.1%
14 21706
6.4%

minute
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.2301
Minimum0
Maximum59
Zeros60696
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:13:42.921270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median29
Q344
95-th percentile58
Maximum59
Range59
Interquartile range (IQR)36

Descriptive statistics

Standard deviation19.300846
Coefficient of variation (CV)0.73582815
Kurtosis-1.235018
Mean26.2301
Median Absolute Deviation (MAD)16
Skewness0.092930947
Sum8833668
Variance372.52264
MonotonicityNot monotonic
2025-05-24T09:13:43.291945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60696
18.0%
30 33899
 
10.1%
45 20398
 
6.1%
15 18868
 
5.6%
55 18834
 
5.6%
59 16288
 
4.8%
10 14503
 
4.3%
25 14450
 
4.3%
5 14118
 
4.2%
29 13823
 
4.1%
Other values (50) 110899
32.9%
ValueCountFrequency (%)
0 60696
18.0%
1 2116
 
0.6%
2 848
 
0.3%
3 1439
 
0.4%
4 1357
 
0.4%
5 14118
 
4.2%
6 1381
 
0.4%
7 1092
 
0.3%
8 1695
 
0.5%
9 1445
 
0.4%
ValueCountFrequency (%)
59 16288
4.8%
58 1065
 
0.3%
57 1388
 
0.4%
56 1713
 
0.5%
55 18834
5.6%
54 1405
 
0.4%
53 1382
 
0.4%
52 1281
 
0.4%
51 1184
 
0.4%
50 12508
3.7%
Distinct6936
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2013-01-01 05:00:00
Maximum2013-12-31 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T09:13:43.719635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:44.176648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-05-24T09:13:27.015276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:48.058514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:51.103522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:54.239538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:57.276064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:00.163452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:03.908653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:07.160659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:10.304074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:13.598823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:17.324754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:20.888551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:23.989984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:27.276943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:48.308951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:51.345534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:54.439057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:57.481179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:00.479945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:04.140223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:07.419060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:10.524741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:13.899092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:17.615257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:21.123325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:24.228484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:27.554590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:48.516545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:51.521286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:54.671178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:57.643585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:00.708469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:04.410658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:07.666655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:10.774070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:14.169116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:17.865136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:21.360733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:24.438599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:27.808581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:48.710369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:51.751728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:54.916925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:57.905275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:00.988990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:04.680059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:07.898908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:11.018768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:14.408629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:18.082873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:21.533771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:24.696004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:28.047394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:48.928491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:51.928514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:55.158710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:58.131548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:01.205871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:04.928502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:08.119808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:11.250118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:14.648574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:18.394563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:21.773603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:24.890908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:28.310136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:49.107474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:52.221468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:55.409050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:58.378696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:01.419169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:05.174521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:08.348577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:11.494262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:14.978531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:18.668694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:22.010663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:25.182232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:28.548554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:49.286971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:52.493535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:55.628661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:58.591259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:01.718444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:05.428461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:08.594051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:11.760062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:15.214085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:18.918722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:22.261868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:25.408690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:28.773764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:49.531088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:52.728910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:55.867094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:58.783990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:02.243994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:05.634010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:08.854823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:11.992804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:15.518901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:19.196257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:22.539546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:25.655328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:29.038824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:49.796806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:52.988697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:56.091241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:59.018776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:02.530018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:05.888811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:09.128093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:12.308697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:15.830169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:19.498520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:22.789405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:25.869827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:29.258306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:49.994443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:53.251598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:56.284368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:59.242221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:02.818868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:06.146449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:09.390083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:12.562666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:16.089713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:19.790566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:23.094086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:26.049942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:29.500175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:50.211479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:53.508744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:56.538832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:59.480871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:03.141848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:06.383795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:09.590539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:12.839866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:16.344264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:20.119188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:23.335757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:26.331954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:29.712522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:50.469937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:53.705163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:56.808745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:59.687053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:03.390816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:06.599957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:09.824865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:13.036596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:16.591521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:20.389384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:23.565742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:26.559084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:29.900057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:50.689777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:53.978752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:57.085494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:12:59.909802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:03.638876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:06.850032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:10.083494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:13.297294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:16.793336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:20.633101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:23.735115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:13:26.762611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-24T09:13:44.535607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
air_timearr_delayarr_timecarrierdaydep_delaydep_timedistanceflighthourminutemonthoriginsched_arr_timesched_dep_time
air_time1.000-0.0230.0570.3580.0030.079-0.0300.984-0.479-0.0320.0340.0050.2450.077-0.029
arr_delay-0.0231.0000.1200.040-0.0000.6260.206-0.0740.0680.1560.023-0.0150.0230.1230.157
arr_time0.0570.1201.0000.106-0.0040.1920.8030.0530.0100.7840.056-0.0040.1160.8710.786
carrier0.3580.0400.1061.0000.0000.0340.1070.3810.4570.1100.1060.0140.5910.1350.112
day0.003-0.000-0.0040.0001.0000.006-0.0000.004-0.000-0.0000.0010.0030.000-0.002-0.000
dep_delay0.0790.6260.1920.0340.0061.0000.2900.076-0.0270.2300.063-0.0160.0190.2170.233
dep_time-0.0300.2060.8030.107-0.0000.2901.000-0.0290.0340.9690.091-0.0040.1090.8770.972
distance0.984-0.0740.0530.3810.0040.076-0.0291.000-0.483-0.0350.0350.0200.2740.072-0.033
flight-0.4790.0680.0100.457-0.000-0.0270.034-0.4831.0000.0320.0040.0040.3200.0020.032
hour-0.0320.1560.7840.110-0.0000.2300.969-0.0350.0321.0000.034-0.0050.0930.8800.998
minute0.0340.0230.0560.1060.0010.0630.0910.0350.0040.0341.0000.0140.1250.0630.095
month0.005-0.015-0.0040.0140.003-0.016-0.0040.0200.004-0.0050.0141.0000.020-0.005-0.005
origin0.2450.0230.1160.5910.0000.0190.1090.2740.3200.0930.1250.0201.0000.1370.115
sched_arr_time0.0770.1230.8710.135-0.0020.2170.8770.0720.0020.8800.063-0.0050.1371.0000.882
sched_dep_time-0.0290.1570.7860.112-0.0000.2330.972-0.0330.0320.9980.095-0.0050.1150.8821.000

Missing values

2025-05-24T09:13:30.285757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T09:13:31.073790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-24T09:13:32.347688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hour
0201311517.05152.0830.081911.0UA1545N14228EWRIAH227.014005151/1/2013 5:00
1201311533.05294.0850.083020.0UA1714N24211LGAIAH227.014165291/1/2013 5:00
2201311542.05402.0923.085033.0AA1141N619AAJFKMIA160.010895401/1/2013 5:00
3201311544.0545-1.01004.01022-18.0B6725N804JBJFKBQN183.015765451/1/2013 5:00
4201311554.0600-6.0812.0837-25.0DL461N668DNLGAATL116.0762601/1/2013 6:00
5201311554.0558-4.0740.072812.0UA1696N39463EWRORD150.07195581/1/2013 5:00
6201311555.0600-5.0913.085419.0B6507N516JBEWRFLL158.01065601/1/2013 6:00
7201311557.0600-3.0709.0723-14.0EV5708N829ASLGAIAD53.0229601/1/2013 6:00
8201311557.0600-3.0838.0846-8.0B679N593JBJFKMCO140.0944601/1/2013 6:00
9201311558.0600-2.0753.07458.0AA301N3ALAALGAORD138.0733601/1/2013 6:00
yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hour
33676620139302240.02250-10.02347.07-20.0B62002N281JBJFKBUF52.0301225030-09-2013 22:00
33676720139302241.02246-5.02345.01-16.0B6486N346JBJFKROC47.0264224630-09-2013 22:00
33676820139302307.0225512.02359.023581.0B6718N565JBJFKBOS33.0187225530-09-2013 22:00
33676920139302349.02359-10.0325.0350-25.0B6745N516JBJFKPSE196.01617235930-09-2013 23:00
3367702013930NaN1842NaNNaN2019NaNEV5274N740EVLGABNANaN764184230-09-2013 18:00
3367712013930NaN1455NaNNaN1634NaN9E3393NaNJFKDCANaN213145530-09-2013 14:00
3367722013930NaN2200NaNNaN2312NaN9E3525NaNLGASYRNaN19822030-09-2013 22:00
3367732013930NaN1210NaNNaN1330NaNMQ3461N535MQLGABNANaN764121030-09-2013 12:00
3367742013930NaN1159NaNNaN1344NaNMQ3572N511MQLGACLENaN419115930-09-2013 11:00
3367752013930NaN840NaNNaN1020NaNMQ3531N839MQLGARDUNaN43184030-09-2013 08:00